Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Label Distribution Learning from Logical Label
Authors: Yuheng Jia, Jiawei Tang, Jiahao Jiang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets prove that the proposed approach can construct a reliable LDL model directly from the logical label, and produce more accurate label distribution than the state-of-the-art LE methods. The code and the supplementary file can be found in https://github.com/seutjw/DLDL. |
| Researcher Affiliation | Academia | Yuheng Jia1,2 , Jiawei Tang1,2 , Jiahao Jiang1,2 1School of Computer Science and Engineering, Southeast University 2Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China |
| Pseudocode | Yes | Algorithm 1 Solve the Problem in (11); Algorithm 2 The DLDL Algorithm |
| Open Source Code | Yes | The code and the supplementary file can be found in https://github.com/seutjw/DLDL. |
| Open Datasets | Yes | We select six real-world datasets from various fields for experiment. Natural Scene (abbr. NS) [Geng, 2016; Geng et al., 2022] is generated from the preference distribution of each scene image, SCUT-FBP (abbr. SCUT) [Xie et al., 2015] is a benchmark dataset for facial beauty perception, RAF-ML (abbr. RAF) [Shang and Deng, 2019] is a multi-label facial expression dataset, SCUT-FBP5500 (abbr. FBP) [Liang et al., 2018] is a big dataset for facial beauty prediction, Ren CECps (abbr. REN) [Quan and Ren, 2009] is a Chinese emotion corpus of weblog articles, and Twitter LDL (abbr. Twitter) [Yang et al., 2017] is a visual sentiment dataset. |
| Dataset Splits | Yes | In this paper, we split each dataset into three subsets: training set (60%), validation set (20%) and testing set (20%). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In the recovery experiment, for DLDL, α and γ are chosen among {10 3, 10 2, , 10, 102}, β is selected from {10 3, 10 2, , 1, 10}, the maximum of iterations t is fixed to 5, the number of neighbors k is set to 20. |